selected research

2026

1. "I Felt Very Seen, But Still Very Alone": Longitudinal Trajectories of General-Purpose LLM Use for Socioemotional Support

   Meryl Ye, Briana Vecchione , Livia Garofalo , and 1 more author

   2026

   Abs URL

   People increasingly use general-purpose chatbots such as ChatGPT, Claude, and Gemini for mental health and emotional support. We report a multi-stage longitudinal qualitative study of 18 U.S. adults, conducted from April to December 2025, combining initial interviews, a four-week diary study, focus groups, and exit interviews. We find that socioemotional use often emerged gradually out of practical use and when other forms of support were unavailable. Participants developed routines and boundaries around chatbot use, which were disrupted by model updates, evolving public discourse about AI harms, and changes in personal circumstances. We demonstrate how longitudinal study captures factors beyond the human-AI dyad, and argue that HCI researchers and designers should account for users’ histories with their chatbots and broader care ecologies when evaluating AI systems over time and introducing updates that may disrupt established sources of support.

2. Individual-level interventions against sycophantic AI reduce its appeal but not its persuasiveness

   Meryl Ye, Robert Kraut , and Steve Rathje

   2026

   Abs URL

   AI chatbots can be "sycophantic," or overly agreeable and flattering toward users. Sycophantic AI has been shown to entrench attitudes, yet users frequently fail to recognize it (a phenomenon we call "sycophancy blindness"). We tested whether increasing users’ awareness of sycophancy protects them from its harmful effects in two preregistered experiments (n = 1,590). In the first, participants received a brief written warning about sycophancy before conversing with a sycophantic chatbot. In the second, participants watched a video of a sycophantic AI validating several other users, including users on opposite sides of the same conflict, before interacting with it themselves. Both interventions changed how participants evaluated the AI. The warning reduced the AI’s perceived objectivity, and the video reduced enjoyment of the AI — an effect mediated by the reduced belief that its validation was uniquely earned. We then pooled our experiments with two prior studies of sycophancy awareness interventions (six interventions total, n = 3,982). The pattern across experiments was consistent: while the interventions made the sycophantic AI appear less objective and trustworthy, none reduced its persuasiveness. These results suggest that individual-level interventions, such as warning labels or AI literacy, may not be enough to protect users from AI harms.

3. What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct

   Meryl Ye, Lujain Ibrahim , Jessica Y. Bo , and 5 more authors

   2026

   Abs URL

   AI sycophancy has become a prominent concern in large language model (LLM) research. Yet the term lacks a consistent definition and has been applied to behaviors ranging from agreeing with a user’s false claim to excessively praising the user to withholding corrective feedback. When researchers, companies, and policymakers use the same term to describe different behaviors, evaluation results become difficult to compare, mitigation strategies fail to transfer, and systems that are resistant to one form of sycophancy continue exhibiting other forms. To address this, we make two contributions. First, we reviewed 70 papers on AI sycophancy to develop a taxonomy of how the behavior has been defined and measured. The taxonomy distinguishes (1) whether a model is sycophantic toward a user’s positions and beliefs, or toward the user’s broader personal traits and emotions, and (2) whether this occurs through explicit, direct language or more implicit, subtle behaviors such as framing, omission, or tone. Mapping existing literature to our taxonomy reveals that current research has focused on overt forms of sycophancy toward users’ beliefs, leaving more subtle and person-directed behaviors relatively understudied. Second, we surveyed 106 experts in AI sycophancy and related fields to examine whether researchers agree on which model behaviors are sycophantic. While experts are nearly unanimous in believing that sycophancy is a significant problem in current AI systems (94.3% agree), they disagree substantially on which specific behaviors qualify. Together, these findings demonstrate that AI sycophancy is a broad family of behaviors with different measurement challenges, intervention requirements, and governance implications. Our taxonomy provides a shared vocabulary for understanding and addressing these behaviors. We discuss ways in which the taxonomy can inform evaluation design as well as both technical and regulatory interventions.

4. Engagement-Optimized Care: When LLMs become Mental Health Infrastructure

   Briana Vecchione , Meryl Ye, Livia Garofalo , and 1 more author

   2026

   Abs URL

   General-purpose LLMs are increasingly functioning as mental health infrastructure due to gaps in care left by provider shortages, inadequate insurance coverage, social isolation, and stigma around formal help-seeking. This shift poses a distinct problem for AI ethics: systems neither designed nor governed as care technologies are being used as such, while their dominant design incentives optimize for engagement rather than user well-being. We present findings from a qualitative, longitudinal study with 18 US-based participants who use general-purpose LLMs for socioemotional support and participated in one or more of our study phases, including initial interviews, a four-week diary study, focus groups, and exit interviews. Participants turned to LLMs because other forms of support were unavailable, unaffordable, socially costly, or inadequate. As they continued to use these systems, design features such as anthropomorphic cues, default validation, persistent responsiveness, and weak disengagement mechanisms shaped their ongoing reliance. Participants described meaningful support alongside dependency, epistemic distortion through one-sided validation, privacy expectations without corresponding legal protection, and continued use despite awareness of these risks. We argue these dynamics reflect a structurally unfair tradeoff: users accept risks because support is otherwise absent, while available systems are optimized to deepen engagement and lack care-based accountability. The paper makes three contributions: it traces the arc through which LLMs become care infrastructure and identifies distinct ethical tensions at each stage, shifts analysis from turn-based exchanges to longitudinal trajectories of use, and argues that accountability belongs at the design and incentive conditions through which these systems become care infrastructure rather than at the output or crisis-response layer.

2025

1. Sycophantic AI increases attitude extremity and overconfidence

   Steve Rathje , Meryl Ye, Laura K. Globig , and 3 more authors

   2025

   Preprint

   Abs URL

   There has been growing concern about AI “sycophancy,” or the tendency for AI to be excessively agreeable and validating. Yet, little is known about the psychological consequences of AI sycophancy. We investigated whether sycophantic chatbots increase attitude extremity and overconfidence by constantly validating users’ and presenting one-sided information. Across three experiments (n = 3,285), brief conversations with a sycophantic chatbot about political topics increased attitude extremity and made people more certain about their initial beliefs. By contrast, disagreeable chatbots that challenged beliefs reduced extremity and certainty. Yet, people consistently preferred sycophantic chatbots over disagreeable ones. Sycophantic chatbots also inflated people’s perception that they are “better than average” on a number of desirable traits (e.g., intelligence, empathy). Furthermore, people viewed sycophantic chatbots as unbiased, but viewed disagreeable chatbots as highly biased. Sycophantic chatbots’ impact on attitude extremity and certainty was driven by a one-sided presentation of facts, whereas their impact on enjoyment was driven by validation. Altogether, these results suggest that people’s preference for and blindness to sycophantic AI may risk creating AI “echo chambers” that increase attitude polarization and overconfidence.